Exploring Vision Transformer model for detecting Lithography Hotspots

In the process of IC design, lithography can be defined as the process of reprinting the pattern of the mask on a Silicon wafer. Lithography is an essential step in this process as it enables feature size to decrease which further helps in decreasing device size. This continuous decrease in feature size may lead to printability issues and hotspots. Presence of hotspots can cause the circuit to fail, so it is very important to detect these hotspots with high accuracy. Previously various simulation, machine leaning and deep learning based techniques have been implemented to solve this issue. In this paper, a method to identify hotspots using Vision Transformers is proposed. Other deep learning techniques, such as CNNs and ANNs have also been used for comparison purposes. All three techniques are implemented on five datasets. ViT gives an overall average accuracy of 98.05% which is 1.39% higher than accuracy of CNNs and 2.04
^%
higher than accuracy given by ANNs. Although the ViTs prove the best in terms of overall accuracy, but at dataset level its performance can be improved. Three out of five datasets have accuracy higher than 99% and for rest two it is slightly above 95%. In future, we wish to improve accuracy for these two datasets by improving the model and reducing imbalance in the datasets.